UniEload:用于孟加拉国公立大学的能源预测应用的电荷数据集
Utshob Sutradhar1, Priyankar Biswas1, Sumon Hossain1
1Department of Electrical and Electronic Engineering, Gopalganj Science and Technology University, Gopalganj-8105, Bangladesh.
Data in brief
|February 2, 2026
概括
本研究介绍了孟加拉国一所大学的开放式电力数据集,有助于能源预测研究. 它包括每小时的电力和天气数据,对于开发人工智能和机器学习模型来实现可持续的校园能源管理至关重要.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 可持续能源 可持续能源
背景情况:
- 发展中国家教育机构提供的有限的公共电力负载数据阻碍了研究.
- 了解校园能源消耗模式对于有效管理和预测至关重要.
研究的目的:
- 来自孟加拉国一所大学校园的电力和相关天气数据的新型,开放式访问数据集.
- 在教育环境中支持能源预测,异常检测和电力效率改进方面的研究.
主要方法:
- 在校园变电站收集每小时的电力数据 (电压,电流,功率因子).
- 集成的每小时同步的天气数据 (温度,湿度,降水,风速,太阳辐射).
- 包括有关运营条件 (学术期间,假期,节假日) 的上下文信息.
主要成果:
- 数据集捕捉了负载行为,功率因子和相位不平衡的变化.
- 能耗和天气参数之间的相关性是可以观察到的.
- 背景说明为事件驱动的负载变化提供了洞察力.
结论:
- 该数据集有助于开发和验证用于能源管理的人工智能和机器学习模型.
- 它解决了关键的数据差距,在高等教育中促进可重复的研究和可持续能源实践.
- 这些数据支持有关天气影响负载预测和异常检测的先进研究.
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